/******************************************************************************* * Copyright (c) 2019-2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author Oleg Semeniv // // #include #include #include #include #include "mkldnnUtils.h" using namespace dnnl; namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////// static void tanhMKLDNN(const NDArray* x, NDArray* z) { const auto xRank = x->rankOf(); dnnl::memory::dims xShape, zShape; mkldnnUtils::getDims(x, xRank, xShape); mkldnnUtils::getDims(z, xRank, zShape); dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank); dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); mkldnnUtils::setBlockStrides(x, xRank, x_user_md); // z dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format); dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format); mkldnnUtils::setBlockStrides(z, xRank, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); // Create attributes (to handle alpha and beta if necessary) dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0) // operation primitive description dnnl::eltwise_forward::desc op_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0); dnnl::eltwise_forward::primitive_desc op_prim_desc(op_desc, attr, engine); // arguments (memory buffers) necessary for calculations std::unordered_map args; dnnl::stream stream(engine); // provide memory buffers and check whether reorder is required // input mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // z auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer()); const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc(); auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem; args[DNNL_ARG_DST] = z_mkl_mem; // run calculations dnnl::eltwise_forward(op_prim_desc).execute(stream, args); // reorder outputs if necessary if (zReorder) dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem); stream.wait(); } PLATFORM_IMPL(tanh, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); const int rank = input->rankOf(); REQUIRE_TRUE(rank <= 6, 0, "TANH_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank); // mkldnnTanh tanhMKLDNN(input, output); return Status::OK(); } PLATFORM_CHECK(tanh, ENGINE_CPU) { auto x = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); const DataType xType = x->dataType(); const DataType zType = z->dataType(); const int xRank = x->rankOf(); bool bSupportedRanks = !x->isEmpty() && xRank < 7 && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32); /* Source Destination f32 f32 */ return block.isUseMKLDNN() && bSupportedRanks; } ////////////////////////////////////////////////////////////////////// static void tanhBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx) { const auto xRank = x->rankOf(); dnnl::memory::dims xShape, dLdzShape, dLdxShape; mkldnnUtils::getDims(x, xRank, xShape); mkldnnUtils::getDims(dLdz, xRank, dLdzShape); mkldnnUtils::getDims(dLdx, xRank, dLdxShape); dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank); dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); mkldnnUtils::setBlockStrides(x, xRank, x_user_md); // dLdz dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); mkldnnUtils::setBlockStrides(dLdz, xRank, dLdz_user_md); // dLdx dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); mkldnnUtils::setBlockStrides(dLdx, xRank, dLdx_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); // arguments (memory buffers) necessary for calculations std::unordered_map args; dnnl::stream stream(engine); // operation primitive description // forward dnnl::eltwise_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0); dnnl::eltwise_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine); // backward description dnnl::eltwise_backward::desc op_desc(algorithm::eltwise_tanh, dLdz_mkl_md, x_mkl_md, 0, 0); dnnl::eltwise_backward::primitive_desc op_prim_desc(op_desc, engine, op_ff_prim_desc); // provide memory buffers and check whether reorder is required for forward // input mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // dLdz mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, args, dLdz_user_md, op_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST); // dLdx auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->getBuffer()); const bool dLdxReorder = op_prim_desc.diff_src_desc() != dLdx_user_mem.get_desc(); auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_prim_desc.diff_src_desc(), engine) : dLdx_user_mem; args[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem; // run calculations backward dnnl::eltwise_backward(op_prim_desc).execute(stream, args); // reorder outputs if necessary if (dLdxReorder) dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem); stream.wait(); } PLATFORM_IMPL(tanh_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto dLdz = INPUT_VARIABLE(1); auto dLdx = OUTPUT_VARIABLE(0); const int rank = input->rankOf(); const int dLdzRank = dLdz->rankOf(); REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "TANH_BP_MKLDNN OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank); // mkldnnSoftMax tanhBpMKLDNN(input, dLdz, dLdx); return Status::OK(); } PLATFORM_CHECK(tanh_bp, ENGINE_CPU) { auto x = INPUT_VARIABLE(0); auto dLdz = INPUT_VARIABLE(1); auto dLdx = OUTPUT_VARIABLE(0); const DataType xType = x->dataType(); const DataType dLdzType = dLdz->dataType(); const DataType dLdxType = dLdx->dataType(); const int xRank = x->rankOf(); const int dLdzRank = dLdz->rankOf(); bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty()); bSupportedRanks &= (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32); if (bSupportedRanks) { for (int i = 0; i < xRank; i++) { if (x->sizeAt(i) != dLdz->sizeAt(i)) { bSupportedRanks = false; break; } } } //Source Destination //f32 f32 return block.isUseMKLDNN() && bSupportedRanks; } } } }